






'''simple docstring'''




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def    __lowercase		(     __lowercase  ,    __lowercase  ,    __lowercase			)			->		Any:

      '''simple docstring'''


      _A     	=      ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

      _A     	=      (
          ("layer.", "layer_"),
          ("word_embeddings.weight", "word_embeddings"),
          ("position_embeddings.weight", "position_embeddings"),
          ("token_type_embeddings.weight", "token_type_embeddings"),
          (".", "/"),
          ("LayerNorm/weight", "LayerNorm/gamma"),
          ("LayerNorm/bias", "LayerNorm/beta"),
          ("weight", "kernel"),
      )

      if not os.path.isdir(__lowercase			):
            os.makedirs(__lowercase			)

      _A     	=      model.state_dict()

      def to_tf_var_name(__lowercase			):
            for patt, repl in iter(__lowercase			):
                  _A     	=      name.replace(__lowercase  ,    __lowercase			)
            return F'''bert/{name}'''

      def create_tf_var(__lowercase  ,    __lowercase  ,    __lowercase			):
            _A     	=      tf.dtypes.as_dtype(tensor.dtype			)
            _A     	=      tf.get_variable(dtype=__lowercase  ,    shape=tensor.shape  ,    name=__lowercase  ,    initializer=tf.zeros_initializer()			)
            session.run(tf.variables_initializer([tf_var]			)			)
            session.run(__lowercase			)
            return tf_var

      tf.reset_default_graph()
      with tf.Session() as session:
            for var_name in state_dict:
                  _A     	=      to_tf_var_name(__lowercase			)
                  _A     	=      state_dict[var_name].numpy()
                  if any(x in var_name for x in tensors_to_transpose			):
                        _A     	=      torch_tensor.T
                  _A     	=      create_tf_var(tensor=__lowercase  ,    name=__lowercase  ,    session=__lowercase			)
                  tf.keras.backend.set_value(__lowercase  ,    __lowercase			)
                  _A     	=      session.run(__lowercase			)
                  print(F'''Successfully created {tf_name}: {np.allclose(__lowercase  ,    __lowercase			)}'''			)

            _A     	=      tf.train.Saver(tf.trainable_variables()			)
            saver.save(__lowercase  ,    os.path.join(__lowercase  ,    model_name.replace("-"  ,    "_"			) + ".ckpt"			)			)

def    __lowercase		(     __lowercase=None			)			->		Tuple:

      '''simple docstring'''


      _A     	=      argparse.ArgumentParser()
      parser.add_argument("--model_name"  ,    type=__lowercase  ,    required=__lowercase  ,    help="model name e.g. bert-base-uncased"			)
      parser.add_argument(
          "--cache_dir"  ,    type=__lowercase  ,    default=__lowercase  ,    required=__lowercase  ,    help="Directory containing pytorch model"			)
      parser.add_argument("--pytorch_model_path"  ,    type=__lowercase  ,    required=__lowercase  ,    help="/path/to/<pytorch-model-name>.bin"			)
      parser.add_argument("--tf_cache_dir"  ,    type=__lowercase  ,    required=__lowercase  ,    help="Directory in which to save tensorflow model"			)
      _A     	=      parser.parse_args(__lowercase			)

      _A     	=      BertModel.from_pretrained(
          pretrained_model_name_or_path=args.model_name  ,    state_dict=torch.load(args.pytorch_model_path			)  ,    cache_dir=args.cache_dir  ,    )

      convert_pytorch_checkpoint_to_tf(model=__lowercase  ,    ckpt_dir=args.tf_cache_dir  ,    model_name=args.model_name			)


if __name__ == "__main__":
    main()






"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__:List[str]									=			logging.getLogger(__name__)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."				)
       parser.add_argument(
           "--dataset_name"   ,    type=a   ,    default="wikitext"   ,    help="Name of the training. Explore datasets at: hf.co/datasets."   ,    )
       parser.add_argument(
           "--dataset_config"   ,    type=a   ,    default="wikitext-103-raw-v1"   ,    help="Configuration name of the dataset."				)
       parser.add_argument(
           "--tokenizer_name_or_path"   ,    type=a   ,    default="sayakpaul/unigram-tokenizer-wikitext"   ,    help="Tokenizer identifier. Can be a local filepath or a Hub identifier."   ,    )
       parser.add_argument(
           "--shard_size"   ,    type=a   ,    default=1_0_0_0   ,    help="Number of entries to go in a single shard."   ,    )
       parser.add_argument("--split"   ,    type=a   ,    default="train"   ,    choices=["train", "test", "validation"]				)
       parser.add_argument(
           "--limit"   ,    default=a   ,    type=a   ,    help="Limit the number of shards (used for debugging)."   ,    )
       parser.add_argument(
           "--max_length"   ,    type=a   ,    default=5_1_2   ,    help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."   ,    )
       parser.add_argument(
           "--output_dir"   ,    default="tf-tpu"   ,    type=a   ,    help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."   ,    )

       __a       =    parser.parse_args()
       return args



def 	_lowerCamelCase(	a				):
       def fn(a				):
              return tokenizer(examples["text"]				)

       return fn



def 	_lowerCamelCase(	a				):
       __a       =    []
       for i in range(len(tokenized_data["input_ids"]				)				):
              __a       =    {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]				)				),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]				)				),
              }
              __a       =    tf.train.Features(feature=a				)
              __a       =    tf.train.Example(features=a				)
              __a       =    example.SerializeToString()
              records.append(a				)
       return records



def 	_lowerCamelCase(	a				):
       __a       =    datasets.load_dataset(args.dataset_name   ,    args.dataset_config   ,    split=args.split				)

       if args.limit is not None:
              __a       =    min(len(a				)   ,    args.limit				)
              __a       =    dataset.select(range(a				)				)
              print(F"Limiting the dataset to {args.limit} entries."				)

       __a       =    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir				):
                     os.makedirs(args.output_dir				)
              __a       =    os.path.join(args.output_dir   ,    args.split				)
              if not os.path.exists(a				):
                     os.makedirs(a				)
       else:
              __a       =    os.path.join(args.output_dir   ,    args.split				)

       # Tokenize the whole dataset at once.
       __a       =    tokenize_function(a				)
       __a       =    dataset.map(a   ,    batched=a   ,    num_proc=4   ,    remove_columns=["text"]				)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(a				):
              # Concatenate all texts.
              __a       =    {k: sum(examples[k]   ,    []				) for k in examples.keys()}
              __a       =    len(concatenated_examples[list(examples.keys()				)[0]]				)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              __a       =    (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              __a       =    {
                  k: [t[i : i + args.max_length] for i in range(0   ,    a   ,    args.max_length				)]
                  for k, t in concatenated_examples.items()
              }
              return result

       __a       =    dataset_tokenized.map(a   ,    batched=a   ,    batch_size=1_0_0_0   ,    num_proc=4				)

       __a       =    0
       __a       =    0
       for shard in range(0   ,    len(a				)   ,    args.shard_size				):
              __a       =    grouped_dataset[shard : shard + args.shard_size]
              __a       =    len(dataset_snapshot["input_ids"]				)
              __a       =    os.path.join(a   ,    F"dataset-{shard_count}-{records_containing}.tfrecord"				)
              __a       =    get_serialized_examples(a				)

              with tf.io.TFRecordWriter(a				) as out_file:
                     for i in range(len(a				)				):
                            __a       =    serialized_examples[i]
                            out_file.write(a				)
                     print("Wrote file {} containing {} records".format(a   ,    a				)				)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"   ,    "w"				) as f:
              print(F"Total {args.split} records: {total_records}"   ,    file=a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			parse_args()
   main(args)
